Descriptive Statistics from the SK Multicultural Data V2

1 Using Multicultural Household Survey

1.1 Number of Naturalized Respondents in the Sample

2 1 [귀화연도 있음] or 2 1 [1. 해당]

# A tibble: 2 × 2
  naturalized_bi        n
  <dbl+lbl>         <int>
1 0 [해당 없음]      9398
2 1 [귀화연도 있음]  8086
# A tibble: 3 × 2
  naturalized_bi       n
  <dbl+lbl>        <int>
1  0 [0. 해당없음] 18419
2  1 [1. 해당]      8296
3 NA                3225
# A tibble: 2 × 2
  naturalized_bi      n
  <dbl+lbl>       <int>
1 0 [0. 해당없음]  7280
2 1 [1. 해당]      8254

1.2 Number of Naturalizations by Year

Figure
Scale for x is already present.
Adding another scale for x, which will replace the existing scale.

1.3 By Country

Figure

Created a data with respondents from Vietnam, Philippines, Thailand, Japan, Mongolia, and China. Dual indicates 1 if the respondent is from Vietnam, Philippines, or Thailand and 0 if the respondent is from Japan, Mongolia, or China.

1.4 By Income

Figure

Do we observe that the changes particularly helped marriage migrants with lower language ability or from poorer households?

Income Table
# A tibble: 9 × 4
  varname  code label            `Monthly Income`
  <chr>   <dbl> <chr>            <chr>           
1 income      1 100만원 미만     $1,000          
2 income      2 100~200만원 미만 $2,000          
3 income      3 200~300만원 미만 $3,000          
4 income      4 300~400만원 미만 $4,000          
5 income      5 400~500만원 미만 $5,000          
6 income      6 500~600만원 미만 $6,000          
7 income      7 600~700만원 미만 $7,000          
8 income      8 700~800만원 미만 $8,000          
9 income      9 800만원 이상     over $8,000     

2 Using Government Statistics

2.1 Number of Naturalized Aliens by Year

Figure

2.2 Number of Naturalized Aliense by Year by Country

Figure
Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)